Presentation at 4S 2013

Matthew Bietz —  November 22, 2013 — Leave a comment

I recently presented on “Personal Genetic Code: Algorithmic Living, Genomics, and the Quantified Self” at the 4S (Society for Social Studies of Science) 2013 Annual Meeting in San Diego, CA. This was work done with Judith Gregory, Scout Calvert, and Geoffrey C. Bowker, in the EVOKE Lab at the UC Irvine Department of Informatics, with funding from the Intel Science and Technology Center for Social Computing.

Abstract: Quantified Self (QS) is a community of individuals who combine personal technologies (wearable sensors, smart phones, etc.) and self-monitoring (of diet, exercise, mood states, etc.) to make sense of their health and well-being. A growth area in QS is the relatively new availability of personal genomic testing. For example, for less than $100 companies like 23andMe will provide a kit for customers to send in a DNA sample that is then analyzed for approximately 250 aspects of health from back pain to breast cancer. Similarly, metagenomic analysis (genomic testing of populations of microorganisms) is being offered as a way to understand the ecosystem of non-human organisms to which our bodies play host. There is a growing awareness within the QS community that personal information can be even more powerful when it is made social. Several QS citizen science projects have started to aggregate personal genomic and metagenomic data to, for example, “publish their test results, find others with similar genetic variations, learn more about their results, find the latest primary literature on their variations and help scientists to find new associations” (http://opensnp.org). In this presentation, we will report on an ongoing study of personal genomics in the QS movement. We will examine the algorithmic rhetoric that surrounds personal genomics, probe how this algorithmic rhetoric is understood and experienced by those who are participating in QS activities, and explore how these participants understand the potential social and scientific benefits and risks of sharing personal health data.

Matthew Bietz

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